Monitoring video quality inside a network

Transcription

1 Monitoring video quality inside a network Amy R. Reibman AT&T Labs Research Florham Park, NJ amy@research.att.com SPS Santa Clara 09 - Page 1 Outline Measuring video quality (inside a network) Anatomy of packet loss impairments (PLI) Estimate MSE due to a PLI Predicting visibility of PLI Conclusions and challenges SPS Santa Clara 09 - Page 2

2 Applications of video quality estimators Algorithm optimization Automated in-the-loop assessment Product benchmarks Vendor comparison to decide what product to buy Product marketing to convince customer to give you $$ System provisioning Determine how many servers, how much bandwidth, etc. Content acquisition and delivery (and SLAs) Enter into legal agreements with other parties Outage detection and troubleshooting SPS Santa Clara 09 - Page 3 Measuring video quality inside the network A video quality monitor for inside the network that is 1. Real-time, 2. Per stream, 3. Scalable to many streams in network, 4. Measures only impact of network impairments, 5. Uses human perceptual properties, and 6. Accurate enough to answer the question: To what degree are specific network impairments affecting the quality of this specific video content? SPS Santa Clara 09 - Page 4

3 Factors that affect video quality Video compression algorithm factors Decoder concealment, packetization, GOP structure, Network-specific factors Delay, Delay variation, Bit-rate, Packet losses Network independent factors Sequence Content, amount of motion, amount of texture, spatial and temporal resolution User Eyesight, interest, experience, involvement, expectations Environmental viewing conditions Background and room lighting; display sensitivity, contrast, and characteristics; viewing distance SPS Santa Clara 09 - Page 5 Where to measure? a Video encoder b network c Video decoder d In the network If corporate network is managed by third party Network operator does not have access to end-systems For videos traversing multiple ISPs Between LAN and WAN, or at access/peering points between ISPs SPS Santa Clara 09 - Page 6

4 What to measure? Not average network performance Different ISPs, Different bandwidth capacities, Different time-varying loads Not only network-level measurements Not all impairments produce same impact Example: some packet losses are invisible, others are highly visible SPS Santa Clara 09 - Page 7 What information can you gather? a Video encoder b network c Video decoder d Original video Encoding parameters Complete encoded bitstream Network impairments (losses, jitter) Lossy bitstream Decoder (concealment, buffer, jitter) Decoded pixels X E(.) E(X) L(.) L(E(X)) D(.) D(L(E(X))) SPS Santa Clara 09 - Page 8

5 Constraints imposed by inside the network Complexity, Scalability If processing too complicated, can t do for all streams Security, Proprietary algorithms If encrypted content, can only process packet headers Structural constraints Some data is unknowable (ex: environmental conditions) Make reasonable assumptions about decoder (buffer handling, error concealment) Measurement point(s) location? Miss impairments between measurement point and viewer Not all measurements may be accurate SPS Santa Clara 09 - Page 9 Categorizing image and video estimators FR NR-P NR-B Full and Reduced Reference (FR and RR) Most available info; requires original and decoded pixels No-Reference Pixel-based methods (NR-P) Requires decoded pixels: a decoder for each video stream No-Reference Bitstream-based methods (NR-B) Processes packets containing bitstream, without decoder SPS Santa Clara 09 - Page 10

6 Traditional FR video quality measurements a Video encoder b network c Video decoder d Original video Encoding parameters Complete encoded bitstream Network impairments (losses, jitter) Lossy bitstream Decoder (concealment, buffer, jitter) Decoded pixels X E(.) E(X) L(.) L(E(X)) D(.) D(L(E(X))) SPS Santa Clara 09 - Page 11 Why doesn t this solve our problem? Full-Reference: uses original and decoded video Needs original video Needs decoded video: a decoder for each stream in network Cannot isolate impact of network impairments Perceptual Full-Reference estimators are REALLY complicated! Lots of parameter settings to get right SPS Santa Clara 09 - Page 12

7 NR-Pixel methods for video quality No-Reference pixel QE: uses only decoded video Still needs a decoder for each stream in network Still cannot isolate impact of network impairments Black-frame detection Video freezes Blockiness (Wu 97, Wang 00, ) Blurriness (Marziliano 02) Jerkiness (Pastrana-Vidal 05, Huynh-Thu 06) Ineffectiveness of error concealment (Yamada 07) Spatial Aliasing (Reibman 08) SPS Santa Clara 09 - Page 13 No-reference Bitstream methods a Video encoder b network c Video decoder d Original video Encoding parameters Complete encoded bitstream Network impairments (losses, jitter) Lossy bitstream Decoder (concealment, buffer, jitter) Decoded pixels X E(.) E(X) L(.) L(E(X)) D(.) D(L(E(X))) SPS Santa Clara 09 - Page 14

8 NR-Bitstream methods for video quality NoParse QuickParse FullParse FullParse No complete decoding, but VLD Mean, variance, spatial correlation, motion vectors Location, extent, duration of losses QuickParse Easy-to-find information only Header information Frame-level (or slice-level) summary information NoParse Network-level stats only SPS Santa Clara 09 - Page 15 ITU-T SG 12 standardization of QoS/QoE P.NAMS Non-intrusive parametric model for quality assessment Only packet-header information (IP through MPEG-2 TS) Useful if payload is encrypted Useful when processing capability is very limited P.NBAMS Non-intrusive bitstream model for quality assessment Allowed to use coded bitstream SPS Santa Clara 09 - Page 16

9 Traditional network-based monitoring a Video encoder b network c Video decoder d Original video Encoding parameters Complete encoded bitstream Network impairments (losses, jitter) Lossy bitstream Decoder (concealment, buffer, jitter) Decoded pixels X E(.) E(X) L(.) L(E(X)) D(.) D(L(E(X))) SPS Santa Clara 09 - Page 17 Why is PLR not enough? For MPEG-2, average MSE is linear with PLR What is the correct slope for a given bitstream? Depends on sequence-specific factors Source content: motion, texture Depends on encoder-specific factors Frequency of Intra information, bit-rate What is specific error for the given loss pattern? Depends on location of specific losses Which frame type, individual spatial and temporal extent, scene change? SPS Santa Clara 09 - Page 18

10 Influence of different content 140 Eight 10-second MPEG-2 sequences, similar bit-rate Sequence MSE Packet Loss Ratio x 10-3 SPS Santa Clara 09 - Page 19 Variation due to different losses Sequence F2 Sequence G4 100 Sequence MSE Packet Loss Ratio x 10-3 SPS Santa Clara 09 - Page 20

11 Quality assessment for networked video Compression effects NR Estimation of MSE due to compression (Turaga 02, Ichigaya 04) Motion-compensated edge artifacts (Leontaris 05) Packet loss effects Estimate MSE (Reibman 02, Naccari 08) Compute Mean Opinion Score (MOS) (Winkler 03, Liu 07, Lin 08) Estimate visibility of individual packet losses (Kanumuri 04) Estimate Mean Time Between Failures (Suresh 05) Timing effects (jitter) Understand delivered video content in streaming scenario (Reibman 04, Gustafsson 08) SPS Santa Clara 09 - Page 21 Estimating MSE due to packet loss MSE = ( fˆ( n, i) ~ f ( n, i)) = e( n, i) N n i N n i where fˆ ( n, i) is encoded value at pixel i frame n ~ and f ( n, i) is decoded value at pixel i frame n and e( n, i) is error for pixel i frame n 2 What clues are in the bitstream to estimate MSE? Map unstructured problem into equivalent structured problem SPS Santa Clara 09 - Page 22

12 Impact of network losses M 0 : set of macroblocks initially lost SPS Santa Clara 09 - Page 23 Impact of network losses M 0 : set of macroblocks initially lost e 0 (n,i) : initial magnitude of error SPS Santa Clara 09 - Page 24

13 Impact of network losses M 0 : set of macroblocks initially lost e 0 (n,i) : initial magnitude of error ψ: prediction process (propagation of error; macroblock type and motion) SPS Santa Clara 09 - Page 25 Impact of network losses M 0 : set of macroblocks initially lost e 0 (n,i) : initial magnitude of error ψ: prediction process (propagation of error; macroblock type and motion) SPS Santa Clara 09 - Page 26

14 Characterization of the error Error is completely characterized by 1. Which macroblocks are initially in error (M 0 ) 2. How large the initial error is in those macroblocks (e 0 (n,i) ) 3. How the error propagates in space and time (ψ) SPS Santa Clara 09 - Page 27 Characterization of the error Error is completely characterized by 1. Which macroblocks are initially in error (M 0 ) Entire picture lost, 1 slice lost, 2 slices lost, 2. How large the initial error is in those macroblocks (e 0 (n,i) ) Depends on source activity (still/moving) Depends on encoder prediction Depends on decoder s concealment strategy 3. How the error propagates in space and time (ψ) Losses in B-frames only impact one frame Received I-frame cleans out previous errors SPS Santa Clara 09 - Page 28

15 Characterization of the error: in the network Error is completely characterized by 1. Which macroblocks are initially in error (M 0 ) Can be measured directly from lossy bitstream (NR-B) Depends on compression, not on video content 2. How large the initial error is in those macroblocks (e 0 (n,i) ) Very hard to estimate accurately from lossy bitstream Can be computed exactly given complete bitstream 3. How the error propagates in space and time (ψ) Characterized by motion vectors, macroblock types Can be extracted exactly from the lossy bitstream (NR-B) SPS Santa Clara 09 - Page 29 Calculating MSE due to packet loss Encoder-based estimation of MSE Uncertainty of loss location, M 0 Exact knowledge of propagation, ψ Exact knowledge of initial error, e 0 (n,i) Bitstream-based estimation of MSE Exact knowledge of location of losses, M 0 Exact knowledge of propagation, ψ Unknown initial error, e 0 (n,i) SPS Santa Clara 09 - Page 30

16 Estimating MSE from lossy bitstream, L(E(X)) Lossy video bitstream: L(E(X)) Extract Bitstream Data New Loss? Yes Estimate Initial Error No Propagate Past Errors MSE estimate (alarm) SPS Santa Clara 09 - Page 31 Extracting bitstream data How deeply can you process the packets? QuickParse: Extracts slice-level information only Frame type, slice location, slice bit-rate, slice quantizer Knows which macroblocks; knows when errors stop Approximates spatial spread of the error propagation FullParse: macroblock-level -- no complete decoding! Mean, variance, spatial correlation, motion vectors Knows exactly which macroblocks and how errors propagate SPS Santa Clara 09 - Page 32

17 Performance comparison: data 225 sample packet loss traces 9 different PLR ranging from 5*10-5 to 5* sample traces per PLR second MPEG-2 sequences Wide range of sensitivity to packet loss 8 sequences in training set; 8 sequences in test set SPS Santa Clara 09 - Page 33 NoParse: Performance 200 Assumes MSE linear with PLR Estimated sequence MSE F1 F2 F3 F4 Correlation 0.71 Probability of error: 9-14% Actual sequence MSE SPS Santa Clara 09 - Page 34

18 QuickParse: Performance 200 Original QuickParse Estimated sequence MSE F1 F2 F3 F4 Correlation 0.79 Probability of error: 8-13% Actual sequence MSE SPS Santa Clara 09 - Page 35 FullParse: Performance 200 FullParse Estimated sequence MSE F1 F2 F3 F4 Correlation 0.95 Probability of error: 3-4% Actual sequence MSE SPS Santa Clara 09 - Page 36

19 Bounds: Estimating MSE from E(X) Lossy video Bitstream, L(E(X)) Info from E(X) Extract Bitstream Data Loss? Yes Use exact initial error No Propagate Past Errors MSE estimate SPS Santa Clara 09 - Page 37 FullParse: Performance 200 FullParse Estimated sequence MSE F1 F2 F3 F4 Correlation 0.95 Probability of error: 3-4% Actual sequence MSE SPS Santa Clara 09 - Page 38

20 FullParse Bound: Performance 200 bound Estimated sequence MSE F1 F2 F3 F4 Correlation Probability of error: 2% Actual sequence MSE SPS Santa Clara 09 - Page 39 QuickParse bound: Performance 200 QuickParse bound Estimated sequence MSE F1 F2 F3 F4 Correlation Probability of error: 2% Actual sequence MSE SPS Santa Clara 09 - Page 40

21 Performance and bounds (16 seqs) Regression Coefficients FullParse FP Bound QP Bound QuickParse SPS Santa Clara 09 - Page 41 Observations: Broadcast MPEG-2 MSE QuickParse: Widely different slopes for different sequences FullParse: More accurate slopes, but room for improvement FullParse bound: Slopes consistently near one, but underestimated QuickParse bound: Nearly same as FullParse bound! Inaccuracy of QuickParse is not due to simpler propagation, but to inaccurate estimate of initial error Reduce the complexity of FullParse Estimate initial error with FP, propagate with QP SPS Santa Clara 09 - Page 42

22 Outline Measuring video quality (inside a network) Anatomy of packet loss impairments (PLI) Estimate MSE due to a PLI Predicting visibility of PLI NOT interested in quality given an average packet loss rate Want to understand impact of each individual packet loss Conclusions and challenges SPS Santa Clara 09 - Page 43 Visibility vs. quality Quality How good is the video? How annoying are the artifacts? Viewers provide MOS on a scale of 1 5 Visibility Did you see an artifact? What fraction of viewers saw artifact? Applications High-quality video transport over a mostly reliable network Design system so that less than some fraction of viewers will notice an impairment in the delivered video stream less than every (time period)? Prioritization of packets to minimize visible impairments SPS Santa Clara 09 - Page 44

23 Three Subjective DataSets Similar strategy (3455 isolated packet losses) Measure each individual packet loss, NOT average quality Testing methodology One packet loss every 4 seconds Viewers are immersed, no audio, CRT display Press the space bar when you see an artifact 12 viewers for every PLI Wide range of parameters Various compression standards (H.264, MPEG-2) Different encoding parameters (Group of Picture, etc) Different approaches for error concealment at decoder SPS Santa Clara 09 - Page 45 Subjective test results: Ground truth 52% of errors seen by no one Number of errors % seen by 3 or fewer 10% seen by 9 or more Number of viewers who saw each error SPS Santa Clara 09 - Page 46

24 Visibility of packet loss impairments Depends on error itself Size, spatial pattern, location, duration, amplitude Depends on decoded signal at location of error New temporal edges (jerkiness), added horizontal edges, broken-up vertical edges Depends on encoded signal at location of error Texture masking, luminance masking, motion masking may hide error Motion tracking may enhance visibility in smoothly moving areas This provides an implicit internal reference, even if not seen SPS Santa Clara 09 - Page 47 Exploratory data analysis (EDA) Visibility as a function of one variable Temporal duration: short one-frame errors are usually invisible 1.5% of one-frame errors are seen by 75%+ of people 63% of one-frame errors are seen by NO ONE! Spatial extent: smaller errors more likely to be invisible Motion: small motion losses typically invisible Initial MSE: smaller errors more likely to be invisible Scene motion: losses more likely to invisible with still camera SPS Santa Clara 09 - Page 48

25 Initial MSE vs. visibility Visible errors Invisible errors probability initial MSE (log) of error SPS Santa Clara 09 - Page 49 Visual Glitch Detector for packet losses Always extract some information for all videos Information about encoded signal Local means and variances, motion, motion accuracy Information about surrounding scene Camera motion; Near a scene change? When there is a packet loss, extract: Information about decoded signal Extra edges possibly introduced Information about error signal Size, duration, initial MSE, initial SSIM Estimate visibility using logistic regression Trained using subjective tests; Humans create ground truth SPS Santa Clara 09 - Page 50

26 Visual Glitch Detector PLD VGD s e T i m e ( c o n d s ) s e T i m e ( c o n d s ) Packet Losses Only Visual Glitch Detector SPS Santa Clara 09 - Page 51 Conclusions Many open problems in measuring video quality Characterizing impact of packet loss using M 0, ψ, and e 0 (n,i) useful in many contexts related to video transport over networks Perceptual quality estimators can be very easy to implement Lots of room for improvement: No-Reference quality estimators that are effective Across different image content and good enough for a legal contract SPS Santa Clara 09 - Page 52

27 Thanks To all my immediate collaborators To E. Koutsofios for lefty graphics package To the community at large To all our subjective test participants To a patient audience SPS Santa Clara 09 - Page 53 Collaborators Broadcast MPEG-2 with losses: MSE Vinay Vaishampayan (AT&T) Swamy Sermadevi (Cornell/Microsoft) Video streaming using Microsoft Media Shubho Sen (AT&T) Kobus van der Merwe (AT&T) Broadcast MPEG-2 with losses: Visibility Sandeep Kanumuri (UCSD/DocomoUSA) Vinay Vaishampayan (AT&T) David Poole (AT&T) Pamela Cosman (UCSD) SPS Santa Clara 09 - Page 54

28 My journal papers on assessing quality A. R. Reibman, Y. Sermadevi and V. Vaishampayan, Quality monitoring of video over a network", IEEE Transactions on Multimedia, vol. 6, no. 2, pp , April S. Kanumuri, P. C. Cosman, A. R. Reibman, and V. Vaishampayan, Modeling packet-loss visibility in MPEG-2 Video", IEEE Transactions on Multimedia, April A. Leontaris, P. C. Cosman, and A. R. Reibman, Quality evaluation of motion-compensated edge artifacts in compressed video", IEEE Transactions on Image Processing, vol. 16, no. 4, pp , April SPS Santa Clara 09 - Page 55 My conference papers on assessing quality A. R. Reibman, Y. Sermadevi and V. Vaishampayan, Quality monitoring of video over the Internet", 36th Asilomar Conference on Signals, Systems, and Computers, vol. 2, pp , Nov A. R. Reibman and V. Vaishampayan, Quality monitoring for compressed video subjected to packet loss", IEEE International Conference on Multimedia and Expo (ICME'03), pp. I , vol. 1, July A. R. Reibman and V. Vaishampayan, ``Low-complexity quality monitoring of MPEG-2 video in a network", IEEE International Conference on Image Processing (ICIP'03), pp. III , Sept A. R. Reibman, S. Kanumuri, V. Vaishampayan, and P. C. Cosman, Visibility of individual packet losses in MPEG-2 video", IEEE International Conference on Image Processing (ICIP'04), pp , Oct S. Kanumuri, P. C. Cosman, and A. R. Reibman, A generalized linear model for MPEG-2 packet-loss visibility", Proc. International Workshop on Packet Video, Dec A. R. Reibman, S. Sen, and J. van der Merwe, Network monitoring for video quality over IP", Proc. Picture Coding Symposium, Dec A. R. Reibman, S. Sen, and J. van der Merwe, Analyzing the spatial quality of Internet streaming video", First International Workshop on Video Processing and Quality Metrics, Scottsdale, AZ, Jan ( A. R. Reibman, S. Sen, and J. van der Merwe, Video quality estimation for Internet streaming", Fourteenth International World Wide Web Conference, Chiba Japan, May A. Leontaris and A. R. Reibman, Comparison of blocking and blurring metrics for video compression", IEEE International Conference on Acoustics, Speech, and Signal Processing, pp , March A. Leontaris, P. C. Cosman, and A. R. Reibman, Measuring the added high frequency energy in compressed video", IEEE International Conference on Image Processing (ICIP'05), pp , Sept A. R. Reibman and T. Schaper, Subjective performance evaluation for super-resolution image enhancement", Second International Workshop on Video Processing and Quality Metrics, Scottsdale, Arizona, January ( A. R. Reibman, R. Bell, and S. Gray, Quality assessment for super-resolution image enhancement", IEEE International Conference on Image Processing, October S. Kanumuri, S. G. Subramanian, P. C. Cosman, and A. R. Reibman, Packet loss visibility in H.264 videos using a reduced reference method", IEEE International Conference on Image Processing, October A. R. Reibman and D. Poole, Characterizing packet-loss impairments in compressed video", IEEE International Conference on Image Processing, Sept A. R. Reibman and D. Poole, Predicting packet-loss visibility using scene characteristics", Sixteenth International Packet Video Workshop, Nov A. R. Reibman and S. Suthaharan, A no-reference spatial aliasing measure for digital image resizing", IEEE International Conference on Image Processing, Oct T.-L. Lin, P. C. Cosman, and A. R. Reibman, Perceptual impact of bursty versus isolated packet losses in H.264 compressed video", IEEE International Conference on Image Processing, Oct SPS Santa Clara 09 - Page 56